1. Feasibility of an artificial intelligence system for tumor response evaluation
- Author
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Nie Xiuli, Chen Hua, Gao Peng, Yu Hairong, Sun Meili, and Yan Peng
- Subjects
RECIST 1.1 ,Artificial intelligence ,Tumor measurement ,Medical technology ,R855-855.5 - Abstract
Abstract Purpose The objective of this study was to evaluate the feasibility of using Artificial Intelligence (AI) to measure the long-diameter of tumors for evaluating treatment response. Methods Our study included 48 patients with lung-specific target lesions and conducted 277 measurements. The radiologists recorded the long-diameter in axial imaging plane of the target lesions for each measurement. Meanwhile, AI software was utilized to measure the long-diameter in both the axial imaging plane and in three dimensions (3D). Statistical analyses including the Bland-Altman plot, Spearman correlation analysis, and paired t-test to ascertain the accuracy and reliability of our findings. Results The Bland-Altman plot showed that the AI measurements had a bias of -0.28 mm and had limits of agreement ranging from − 13.78 to 13.22 mm (P = 0.497), indicating agreement with the manual measurements. However, there was no agreement between the 3D measurements and the manual measurements, with P
- Published
- 2024
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